Although some frameworks are available to compare point clouds via discrete transport [1]} [2]}, interpretability is often limited by the high dimensionality of the embedding space. Also, model based approaches, which capture the variability of cloud-generating processes by means of interpretable parameters, require a high number of observations in each point cloud to produce reliable estimations [3]}.
